Comparison of NARX Neural Network and Classical Modelling Approaches

Gaya, Muhammad Sani; Wahab, Norhaliza Abdul; Sam, Yahaya Md; Samsudin, Sharatul Izah; Jamaludin, Irma Wani

doi:10.4028/www.scientific.net/amm.554.360

Cited by 14 publications

(3 citation statements)

References 10 publications

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“…The NARX technique improves learning performance and computing efficiency in addressing battery nonlinearity. Its predicted output is consistently validated against the true value, enhancing accuracy in time series forecasts by storing both input and previous output values as feedback [33]. Fig.…”

Section: Narx Modelmentioning

confidence: 95%

Measurement of State of Charge of Lithium-Nickel Manganese Cobalt Battery using Artificial Neural Network and NARX Algorithm

et al. 2024

Int. j. electr. comput. eng. syst. (Online)

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The battery's SoC is a crucial variable since it reflects its performance. An accurate estimation of SoC protects the battery, prevents overcharging or discharge, and extends its life time. Since most of the traditional methods use complex equations, ANN has been implemented to reduce the complications and provide better accuracy. In this research, Li-NMC with capacity rating of 2000mAh is used for the estimation of SoC. In this paper, Feedforward Neural Network (FNN) algorithm and Nonlinear Auto-Regressive network with exogenous inputs (NARX) have been used for designing a neural network model. Here, the performance matrixes of both neural network models have been compared and analyzed with the same dataset.

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Section: Narx Modelmentioning

confidence: 95%

Measurement of State of Charge of Lithium-Nickel Manganese Cobalt Battery using Artificial Neural Network and NARX Algorithm

et al. 2024

Int. j. electr. comput. eng. syst. (Online)

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“…NDBI data was also used to support the validation of the model results. be highlighted that when compared to conventional feedback network architectures, it converges faster and offers more effective learning (Sani et al 2014;Gündoğdu 2020).…”

Section: Ndbimentioning

confidence: 99%

Evaluation of Prediction Performance of Vegetation Biomass Density for Two Different Case Study Areas in Turkey with Hybrid Wavelet and Artificial Neural Network Method

İşler¹,

Aslan

Sunar

et al. 2023

Preprint

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Deterioration of natural resources such as vegetation, due to urbanization and increasing population density is evident in many areas of the world. For land use planning, it is vital to assess the plant density and forecast its future changes in light of vegetation-climate interactions given the current trend of global climate change. The purpose of this article is to show how we can detect the variation of vegetation density and forecast its future values with the Artificial Intelligence (AI) methods. As case studies, we selected 2 districts namely Alanya in Antalya province and Iznik in Bursa province of Turkey, that showed the highest and lowest land cover change between 2006 and 2018 respectively, according to the CORINE land cover classification. In the analysis, we have used satellite data (Enhanced Vegetation Index (EVI) and Land Surface Temperature (LST) data from MODIS/Terra satellite) and atmospheric data (archive precipitation and temperature measurements at meteorological stations) to define vegetation changes up to 2030.We have used ANN with original data and with the data obtained by the wavelet transform application (W-ANN). The average EVI value for 2030 was calculated as 0.22 with a 5.4% error probability for Iznik, and 0.28 with a 2% error probability for Alanya. By comparing the predicted values of W-ANN for 2030 with respect those of 2018, vegetation biomass density will decrease by 21.4% in Iznik, and 6.6% in Alanya. The results were also compared with the Landsat Normalized Difference Built-up Index (NDBI).

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“…Specifically, this study proposes the utilization of Nonlinear Autoregressive Exogenous (NARX) Artificial Neural Networks (ANNs). The NARX model is particularly suitable for handling nonlinearities and uncertainties, making it a popular choice in time-series forecasting, control systems, and nonlinear system identification [67]. Two approaches are employed here combining NARX neural networks with MPC [68], [69]:…”

mentioning

confidence: 99%

Neural-Network Based MPC for Enhanced Lateral Stability in Electric Vehicles

Hassan,

Ruiz-Moreno,

Frejo

et al. 2024

IEEE Access

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Distributed electric drive vehicles offer maneuver-ability but face stability challenges under different driving conditions. Model Predictive Control (MPC) algorithms can improve lateral stability, but their high computational demands hinder real-time implementation. To address this, the proposed strategy combines Nonlinear Autoregressive Exogenous (NARX) neural networks with MPC in two ways, namely, Nonlinear Prediction-Nonlinear Optimization (NMPC-NO) and Nonlinear Prediction-Linearization (MPC-NPL). While NMPC-NO involves online nonlinear optimization, MPC-NPL uses local linearization, reducing both the computational load significantly to about 40% of the computation time of MPC and 0.05% of that of nonlinear model predictive control (NMPC). The neural networks are trained and validated on 20 different datasets, with alternative training methods investigated. MATLAB/Simulink simulations under various standardized tests demonstrate the effectiveness of the proposed techniques, highlighting improved handling performance, reduced computation time, and real-time deployment capabilities. Recent research underscores Direct Yaw Moment Control (DYC) as effective for improving handling stability [6]-[8]. DYC comprises two control levels, with the upper controller

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Comparison of NARX Neural Network and Classical Modelling Approaches

Cited by 14 publications

References 10 publications

Measurement of State of Charge of Lithium-Nickel Manganese Cobalt Battery using Artificial Neural Network and NARX Algorithm

Measurement of State of Charge of Lithium-Nickel Manganese Cobalt Battery using Artificial Neural Network and NARX Algorithm

Evaluation of Prediction Performance of Vegetation Biomass Density for Two Different Case Study Areas in Turkey with Hybrid Wavelet and Artificial Neural Network Method

Neural-Network Based MPC for Enhanced Lateral Stability in Electric Vehicles

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